Automatic Speech Recognition
Transformers
TensorBoard
Safetensors
English
whisper
Generated from Trainer
Eval Results (legacy)
Instructions to use Noursene/whisper-tiny-2000 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Noursene/whisper-tiny-2000 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="Noursene/whisper-tiny-2000")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("Noursene/whisper-tiny-2000") model = AutoModelForSpeechSeq2Seq.from_pretrained("Noursene/whisper-tiny-2000", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from Noursene/whisper-tiny-2000: direct link, hf CLI and curl.
- Browser
- Download file 5.11 kB
-
https://huggingface.co/Noursene/whisper-tiny-2000/resolve/main/training_args.bin
- Command line
-
hf download hf://Noursene/whisper-tiny-2000/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/Noursene/whisper-tiny-2000/resolve/main/training_args.bin
5.11 kB
- Xet hash:
- 453747f6b742ea4560f6d782c86fe79b28820f1ce284866710cc0640fa54cdc9
- Size of remote file:
- 5.11 kB
- SHA256:
- 8d8be077febb1ee9f7f011c503f291bac3d4d409f39c0bcad4e56fd524590060
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.